New Evaluation Setup:    Here we design a new disjoint train-test split (DS) in addition to conventional train-test split (CS). While training we remove all words from MJSynth and Synth90K whose truth-pairs appear in any of the mentioned STR testing datasets. Additionally we ensure, \(\mathcal {G}\)  does not contain any information from the testing set. Due to size constraint in HTR datasets, we split it such that all word-image pairs corresponding to one particular ground-truth character sequence must collectively fall in either one of training or testing set, thus ensuring disjointedness. This evaluation protocol grades our model's recognition performance on word images whose ground-truth character sequences never appeared in the training dataset. We use it to verify generalising capability of our model for unseen or rarely seen words. Furthermore, superiority of our model in this scenario confirms a fair result on datasets having less unique words. As large datasets are rarely available for text recognition (specifically handwriting) apart from English, it is ideal to use a dataset created by collecting multiple instances of a small set of unique words which thus need to be annotated just once per unique word.
To do so any model needs to learn character-specific fine-grained details from a small available set of unique words, and generalise onto other unseen character sequences. Please note that we re-train SOTA models [1]}, [2]}, [3]}, [4]}, used to collect the error distribution, by ensuring no words from evaluation set appear in their training (ensuring no “leak" of information), while collecting error distribution for DS setup.
